TY - GEN
T1 - Using gene pair combinations to improve the accuracy of the PAM classifier
AU - Chopra, Pankaj
AU - Kang, Jaewoo
AU - Lee, Jinseung
PY - 2009
Y1 - 2009
N2 - Various classification methods have been used to predict the class of tissue samples based on gene expression data. Prediction Analysis for Microarrays (PAM) is one of the top classifiers that has been extensively used for cancer classification. In this paper a novel method of combining expression data from gene pairs is used to improve the overall accuracy of PAM. Recent studies suggest that deregulation of pathways, rather than individual genes, may be critical in triggering carcinogenesis. The pathway deregulation is often caused by the simultaneous deregulation of more than one genes in the pathway. Robust gene pair combinations may exploit these underlying bio-molecular reactions to provide better biomarkers for cancer, as compared to single genes. In this work, we used gene pair combinations, called doublets, to improve the accuracy of PAM. We validated the proposed approach with nine cancer datasets. The accuracy of PAM, using these doublets, increased consistently across these datasets, in some cases with a significant margin (13%).
AB - Various classification methods have been used to predict the class of tissue samples based on gene expression data. Prediction Analysis for Microarrays (PAM) is one of the top classifiers that has been extensively used for cancer classification. In this paper a novel method of combining expression data from gene pairs is used to improve the overall accuracy of PAM. Recent studies suggest that deregulation of pathways, rather than individual genes, may be critical in triggering carcinogenesis. The pathway deregulation is often caused by the simultaneous deregulation of more than one genes in the pathway. Robust gene pair combinations may exploit these underlying bio-molecular reactions to provide better biomarkers for cancer, as compared to single genes. In this work, we used gene pair combinations, called doublets, to improve the accuracy of PAM. We validated the proposed approach with nine cancer datasets. The accuracy of PAM, using these doublets, increased consistently across these datasets, in some cases with a significant margin (13%).
KW - Cancer classification
KW - Doublets
KW - Gene pairs
KW - Microarray
KW - PAM
UR - http://www.scopus.com/inward/record.url?scp=74549199332&partnerID=8YFLogxK
U2 - 10.1109/BIBM.2009.47
DO - 10.1109/BIBM.2009.47
M3 - Conference contribution
AN - SCOPUS:74549199332
SN - 9780769538853
T3 - 2009 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2009
SP - 174
EP - 177
BT - 2009 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2009
T2 - 2009 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2009
Y2 - 1 November 2009 through 4 November 2009
ER -
